{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 没有明显报错的正常的log输出 60分。 用的代码类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##########\n",
    "step [100], entropy loss: [1.8077820539474487]\n",
    "0.75\n",
    "0.7575\n",
    "##########\n",
    "step [200], entropy loss: [1.2281439304351807]\n",
    "0.84375\n",
    "0.8322\n",
    "##########\n",
    "step [300], entropy loss: [0.6806436777114868]\n",
    "0.9375\n",
    "0.8607\n",
    "##########\n",
    "step [400], entropy loss: [0.2826918959617615]\n",
    "0.96875\n",
    "0.8646\n",
    "##########\n",
    "step [500], entropy loss: [0.5611040592193604]\n",
    "0.96875\n",
    "0.8744\n",
    "##########\n",
    "step [600], entropy loss: [1.247020959854126]\n",
    "0.84375\n",
    "0.864\n",
    "##########\n",
    "step [700], entropy loss: [0.5256452560424805]\n",
    "0.9375\n",
    "0.86\n",
    "##########\n",
    "step [800], entropy loss: [0.4076303243637085]\n",
    "0.96875\n",
    "0.8868\n",
    "##########\n",
    "step [900], entropy loss: [0.2910122573375702]\n",
    "1.0\n",
    "0.8855\n",
    "##########\n",
    "step [1000], entropy loss: [0.30914562940597534]\n",
    "1.0\n",
    "0.8888\n",
    "##########\n",
    "step [1100], entropy loss: [0.16916915774345398]\n",
    "0.96875\n",
    "0.8925\n",
    "##########\n",
    "step [1200], entropy loss: [0.4044695496559143]\n",
    "0.9375\n",
    "0.8968\n",
    "##########\n",
    "step [1300], entropy loss: [0.3257879614830017]\n",
    "0.9375\n",
    "0.8998\n",
    "##########\n",
    "step [1400], entropy loss: [0.9972343444824219]\n",
    "0.78125\n",
    "0.8958\n",
    "##########\n",
    "step [1500], entropy loss: [0.7232746481895447]\n",
    "0.875\n",
    "0.8982\n",
    "##########\n",
    "step [1600], entropy loss: [0.7982097864151001]\n",
    "0.9375\n",
    "0.8955\n",
    "##########\n",
    "step [1700], entropy loss: [0.5121141076087952]\n",
    "0.875\n",
    "0.9003\n",
    "##########\n",
    "step [1800], entropy loss: [0.5033940076828003]\n",
    "0.84375\n",
    "0.9001\n",
    "##########\n",
    "step [1900], entropy loss: [0.9314243793487549]\n",
    "0.78125\n",
    "0.8966\n",
    "##########\n",
    "step [2000], entropy loss: [0.31906020641326904]\n",
    "0.90625\n",
    "0.8975\n",
    "##########\n",
    "step [2100], entropy loss: [0.15459030866622925]\n",
    "0.96875\n",
    "0.8979\n",
    "##########\n",
    "step [2200], entropy loss: [1.3047972917556763]\n",
    "0.75\n",
    "0.8994\n",
    "##########\n",
    "step [2300], entropy loss: [0.9003318548202515]\n",
    "0.8125\n",
    "0.9008\n",
    "##########\n",
    "step [2400], entropy loss: [0.26521772146224976]\n",
    "0.875\n",
    "0.9009\n",
    "##########\n",
    "step [2500], entropy loss: [0.7924585938453674]\n",
    "0.96875\n",
    "0.9021\n",
    "##########\n",
    "step [2600], entropy loss: [0.5020565986633301]\n",
    "0.9375\n",
    "0.9013\n",
    "##########\n",
    "step [2700], entropy loss: [0.7550867795944214]\n",
    "0.875\n",
    "0.9031\n",
    "##########\n",
    "step [2800], entropy loss: [0.35947567224502563]\n",
    "0.875\n",
    "0.9008\n",
    "##########\n",
    "step [2900], entropy loss: [0.3657664656639099]\n",
    "0.9375\n",
    "0.9043\n",
    "##########\n",
    "step [3000], entropy loss: [0.0903657004237175]\n",
    "0.96875\n",
    "0.9051"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对模型结构的理解10分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "激活函数：softmax\n",
    "模型输出的是logit， 而实际的标签为y， 它们应当越相似越好，用交叉熵损失来衡量这种相似性，损失越小模型的输出就和实际标签越接近，模型的预测也就越准确，用梯度下降法优化损失，\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对模型训练过程（梯度如何计算，参数如何更新）的理解10分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow默认会对所有变量计算梯度，变量w和b将会被使用梯度下降而更新他们的值。\n",
    "在优化前，必须要创建一个会话（session），并在会话中对变量进行初始化操作\n",
    "有了会话，就可以对变量W， b进行优化.\n",
    "计算accuracy和entropy loss。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对计算图的理解10分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow程序一般可以分为两个阶段。在第一个阶段需要定义计算图中所有的计算，然后定义了一个计算来得到它们的和。第二阶段为执行计算。\n",
    "这里有一个计算图，里面有计算节点信息，每个操作为一个node，w，b都是参数，都是节点\n",
    "计算图是一个有向图，由一组节点，一组有向边组成。\n",
    "创建计算图就是建立计算模型，执行对话才能提供数据并获得结果。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 解释这里的模型为什么效果比较差10分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习率较大,后期会造成较大波动出现围绕最优值徘徊而无法收敛的情况。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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